Digital hair segmentation using hybrid convolutional and recurrent neural networks architecture Attia, Mohammed Hassan, Hossny, Mohammed, Zhou, Hailing, Nahavandi, Saeid, Asadi, Hamed and Yazdabadi, Anosha 2019, Digital hair segmentation using hybrid convolutional and recurrent neural networks architecture, Computer methods and programs in biomedicine, vol. 177, pp. 17-30, doi: 10.1016/j.cmpb.2019.05.010. Attached Files Name Description MIMEType Size Downloads Title Digital hair segmentation using hybrid convolutional and recurrent neural networks architecture Author(s) Attia, Mohammed Hassan orcid.org/0000-0002-4803-2963 Hossny, Mohammed orcid.org/0000-0002-1593-6296 Zhou, Hailing orcid.org/0000-0001-5009-4330 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Asadi, Hamed orcid.org/0000-0003-2475-9727 Yazdabadi, Anosha Journal name Computer methods and programs in biomedicine Volume number 177 Start page 17 End page 30 Total pages 14 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2019-08 ISSN 0169-2607 1872-7565 Keyword(s) Dermatology Hair detection Hair segmentation Deep learning Language eng DOI 10.1016/j.cmpb.2019.05.010 Indigenous content off Field of Research 0903 Biomedical Engineering HERDC Research category C1.1 Refereed article in a scholarly journal Copyright notice ©2019, Elsevier B.V. Persistent URL http://hdl.handle.net/10536/DRO/DU:30122879 Document type: Journal Article Collections: Centre for Intelligent Systems Research GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Fri, 14 Jun 2019, 14:20:03 EST Sat, 15 Jun 2019, 05:03:19 EST Mon, 17 Jun 2019, 08:18:31 EST Fri, 26 Jul 2019, 06:02:46 EST Wed, 31 Jul 2019, 10:30:53 EST Wed, 31 Jul 2019, 10:34:06 EST Sat, 10 Aug 2019, 02:30:32 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 39 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Fri, 14 Jun 2019, 14:20:03 EST

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